


'''simple docstring'''


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				def is_in_circle(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	) -> bool:
								_SCREAMING_SNAKE_CASE					:			List[str]							= sqrt((x**2) + (y**2)	)
								# Our circle has a radius of 1, so a distance
								# greater than 1 would land outside the circle.
								return distance_from_centre <= 1

				# The proportion of guesses that landed in the circle
				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= mean(
				    int(is_in_circle(uniform(-1.0				, 1.0	)				, uniform(-1.0				, 1.0	)	)	)
				    for _ in range(SCREAMING_SNAKE_CASE__	)	)
				# The ratio of the area for circle to square is pi/4.
				_SCREAMING_SNAKE_CASE					:			str							= proportion * 4
				print(f"""The estimated value of pi is {pi_estimate}"""	)
				print(f"""The numpy value of pi is {pi}"""	)
				print(f"""The total error is {abs(pi - pi_estimate	)}"""	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__ = 0.0				, SCREAMING_SNAKE_CASE__ = 1.0				, ):







				"""simple docstring"""





				return mean(
				    function_to_integrate(uniform(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)	) for _ in range(SCREAMING_SNAKE_CASE__	)	) * (max_value - min_value)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__ = 0.0				, SCREAMING_SNAKE_CASE__ = 1.0	):







				"""simple docstring"""





				def identity_function(SCREAMING_SNAKE_CASE__	) -> float:
								return x

				_SCREAMING_SNAKE_CASE					:			List[str]							= area_under_curve_estimator(
				    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Any							= (max_value * max_value - min_value * min_value) / 2

				print("""******************"""	)
				print(f"""Estimating area under y=x where x varies from {min_value} to {max_value}"""	)
				print(f"""Estimated value is {estimated_value}"""	)
				print(f"""Expected value is {expected_value}"""	)
				print(f"""Total error is {abs(estimated_value - expected_value	)}"""	)
				print("""******************"""	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				def function_to_integrate(SCREAMING_SNAKE_CASE__	) -> float:
								return sqrt(4.0 - x * x	)

				_SCREAMING_SNAKE_CASE					:			int							= area_under_curve_estimator(
				    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, 0.0				, 2.0	)

				print("""******************"""	)
				print("""Estimating pi using area_under_curve_estimator"""	)
				print(f"""Estimated value is {estimated_value}"""	)
				print(f"""Expected value is {pi}"""	)
				print(f"""Total error is {abs(estimated_value - pi	)}"""	)
				print("""******************"""	)


if __name__ == "__main__":
				import doctest

				doctest.testmod()








'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_						:  str				  =						logging.get_logger(__name__)

UpperCAmelCase_						:  Optional[int]				  =						{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: List[str]       =    """lilt"""






							def __init__(       self       ,   __snake_case=3_0522       ,   __snake_case=768       ,   __snake_case=12       ,   __snake_case=12       ,   __snake_case=3072       ,   __snake_case="gelu"       ,   __snake_case=0.1       ,   __snake_case=0.1       ,   __snake_case=512       ,   __snake_case=2       ,   __snake_case=0.02       ,   __snake_case=1e-12       ,   __snake_case=0       ,   __snake_case="absolute"       ,   __snake_case=None       ,   __snake_case=4       ,   __snake_case=1024       ,   **__snake_case       ,   ):
											super().__init__(pad_token_id=__snake_case       ,   **__snake_case    )

											_SCREAMING_SNAKE_CASE					:			str							= vocab_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= num_hidden_layers
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= num_attention_heads
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_act
											_SCREAMING_SNAKE_CASE					:			Dict							= intermediate_size
											_SCREAMING_SNAKE_CASE					:			Tuple							= hidden_dropout_prob
											_SCREAMING_SNAKE_CASE					:			List[str]							= attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= max_position_embeddings
											_SCREAMING_SNAKE_CASE					:			Dict							= type_vocab_size
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= initializer_range
											_SCREAMING_SNAKE_CASE					:			Tuple							= layer_norm_eps
											_SCREAMING_SNAKE_CASE					:			str							= position_embedding_type
											_SCREAMING_SNAKE_CASE					:			List[str]							= classifier_dropout
											_SCREAMING_SNAKE_CASE					:			int							= channel_shrink_ratio
											_SCREAMING_SNAKE_CASE					:			str							= max_ad_position_embeddings




